Skip to main content

Machine Learning tool allowing plug-and-play training for pytorch models

Project description

pyroml

🔥 Machine Learning framework allowing plug-and-play training for pytorch models

Installation

$ git clone https://github.com/peacefulotter/pyroml.git
$ cd pyroml
$ sudo apt install python3.10-venv # check you python version and change it here if !=
$ sudo apt install python3-virtualenv
$ python3 -m venv venv
$ source ./venv/bin/activate
$ pip install -r requirements.txt

Running tests

$ cd tests
$ python main.py # this will launch the training, follow the wandb link to access the plots
$ python pretrain.py # will load the last checkpoint and compute mse on a small part of the dataset, outputs True if model predicts correctly!

Done

  • Metrics, with support for custom metrics
  • WandB
  • Checkpoints
  • Load pretrained models from checkpoints

TODO:

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

pyroml-0.0.7.tar.gz (8.9 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

pyroml-0.0.7-py3-none-any.whl (9.8 kB view details)

Uploaded Python 3

File details

Details for the file pyroml-0.0.7.tar.gz.

File metadata

  • Download URL: pyroml-0.0.7.tar.gz
  • Upload date:
  • Size: 8.9 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.10.12

File hashes

Hashes for pyroml-0.0.7.tar.gz
Algorithm Hash digest
SHA256 3218e312fadc579f976827cdf8e12953694d21ea973ef1027c3d26b1df754c03
MD5 03ce1fed945c2fbd52821025f65f93bf
BLAKE2b-256 afcfa2338c12abd5551d55a1bfdd213d68444fe1030f912cc6d0d9e74d8076e1

See more details on using hashes here.

File details

Details for the file pyroml-0.0.7-py3-none-any.whl.

File metadata

  • Download URL: pyroml-0.0.7-py3-none-any.whl
  • Upload date:
  • Size: 9.8 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.10.12

File hashes

Hashes for pyroml-0.0.7-py3-none-any.whl
Algorithm Hash digest
SHA256 587ce16671213f24d76ad14fd46fe175ef5e1236e96bc7845bd5e825e22213bc
MD5 9e9e102a353ea8c30e5573ef49d90d3c
BLAKE2b-256 4d6ae914a85046e23748fb188be67bcc7f497bbdf56cb9ca5a295924d9fac91e

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page